ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox
arXiv:2605.10787v2 Announce Type: replace Abstract: Current LLM agents are proficient at calling isolated APIs but struggle with the "last mile" of commercial software automation. In real-world scenarios, tools are not independent; they are atomic, interdependent, and prone to environmental noise. We introduce $\textbf{ComplexMCP}$, a benchmark designed to evaluate agents in these rigorous conditions. Built on the Model Context Protocol (MCP), $\textbf{ComplexMCP}$ provides over 300 meticulously tested tools derived from 7 stateful sandboxes, ranging from office suites to financial systems. Un
The rapid advancement of large language models and their increasing integration into autonomous systems makes the evaluation of their real-world capabilities, especially in complex environments, a critical next step.
This benchmark addresses a major limitation in current AI agent development, moving beyond isolated API calls to evaluate performance in interdependent and dynamic software automation scenarios.
The introduction of ComplexMCP shifts the focus of LLM agent development and evaluation towards more robust, generalized, and real-world applicable performance, scrutinizing their ability to navigate complex software ecosystems.
- · AI agent developers focused on enterprise automation
- · Open-source AI research community
- · Businesses seeking advanced automation solutions
- · AI agent providers with limited real-world testing
- · Legacy automation platforms
- · Companies relying on isolated API integrations
Improved LLM agents capable of handling complex, stateful, and interdependent tasks in commercial software.
Acceleration of 'lights-out' automation across a wider range of white-collar workflows, reducing the need for human intervention in highly structured but complex digital environments.
Increased demand for robust, secure, and well-documented API ecosystems that agents can reliably interact with, influencing software design principles.
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Read at arXiv cs.AI